DESCRIPTION

Package: spectralGraphTopology
Title: Learning Graphs from Data via Spectral Constraints
Version: 0.2.3
Date: 2022-03-12
Description: In the era of big data and hyperconnectivity, learning
    high-dimensional structures such as graphs from data has become a prominent
    task in machine learning and has found applications in many fields such as
    finance, health care, and networks. 'spectralGraphTopology' is an open source,
    documented, and well-tested R package for learning graphs from data. It
    provides implementations of state of the art algorithms such as Combinatorial
    Graph Laplacian Learning (CGL), Spectral Graph Learning (SGL), Graph Estimation
    based on Majorization-Minimization (GLE-MM), and Graph Estimation based on
    Alternating Direction Method of Multipliers (GLE-ADMM). In addition, graph
    learning has been widely employed for clustering, where specific algorithms
    are available in the literature. To this end, we provide an implementation of
    the Constrained Laplacian Rank (CLR) algorithm.
Authors@R: c(
  person("Ze", "Vinicius", role = c("cre", "aut"), email = "jvmirca@gmail.com"),
  person(c("Daniel", "P."), "Palomar", role = "aut", email = "daniel.p.palomar@gmail.com")
  )
Maintainer: Ze Vinicius <jvmirca@gmail.com>
URL: https://github.com/dppalomar/spectralGraphTopology,
     https://mirca.github.io/spectralGraphTopology/,
     https://www.danielppalomar.com
BugReports: https://github.com/dppalomar/spectralGraphTopology/issues
Depends:
License: GPL-3
Encoding: UTF-8
LazyData: true
LinkingTo:
    Rcpp,
    RcppArmadillo,
    RcppEigen
Imports:
    CVXR,
    Rcpp (>= 0.11.0),
    MASS,
    Matrix,
    progress,
    rlist
RoxygenNote: 7.1.1
Suggests:
    bookdown,
    knitr,
    prettydoc,
    rmarkdown,
    R.rsp,
    testthat,
    patrick,
    corrplot,
    igraph,
    kernlab,
    pals,
    clusterSim,
    viridis,
    quadprog,
    matrixcalc
VignetteBuilder:
    CVXR,
    knitr,
    rmarkdown,
    R.rsp